一种基于累积量的ESPRIT型盲波束形成算法

讨论一种用于非高斯信号源方位估计的盲波束形成算法。假设背景噪声是二阶统计未知的有色(空间相关)高斯噪声,基于“阵元对”模型的ESPRIT方位估计算法可以通过用累积量矩阵取代自相关矩阵完成信号空间的重建。经过这种处理,加性有色高斯噪声可被滤除,因此算法不需要相关噪声矩阵的知识。当加性噪声源是空间相关矩阵未知的有色高斯噪声时,计算机仿真比较了基于累积量的ESPRIT算法与基于二阶统计的ESPRIT算法的性能。     

矢量水听器阵时频MUSIC算法研究

时频MUSIC算法利用信号的时频分布构造空间时频分布矩阵,并用该矩阵代替传统的相关矩阵进行DOA估计,可以有效抑制噪声和干扰,提高算法的稳健性。时频子空间算法突破了传统子空间算法中阵元数对估计信号个数的限制,时频点包含了信号的时频空三维信息,通过时频点的选择可直接确定信号的频率从而确定阵列流型矩阵。对于宽带信号,在进行方位估计时避免了频域搜索,减少了运算量。将时频MUSIC算法应用于二维矢量水听器垂直线阵中,充分利用矢量水听器的标、矢量信息和信号的时、频信息进行宽带信号的二维波达方位估计。仿真研究验证了算法的有效性。     

基于空时相关阵联合对角化的子空间DOA估计

为提高空间相关噪声场中的目标方位估计性能,提出一种基于空时相关阵联合块对角化的子空间方位估计算法.具体利用Jacobi旋转矩阵法对一组空时相关阵联合近似对角化,用联合对角化特征向量矩阵和特征值修正MUSIC(Multiple Signal Classification)等子空间算法.理论和仿真结果表明,在非相关噪声场中,基于联合对角化的子空间算法性能与常规子空间算法基本一致;而在相关噪声场中,联合对角化特征向量法能显著减小方位估计方差,提高估计性能.     

自适应噪声抵消在矢量传感器方位估计中的应用

矢量传感器能同时拾取声压和质点振速信息．单个矢量传感器就可以估计目标方位,因此非常适合在一些小平台上使用。一般平台的自噪声非常大,这对于目标的方位估计很不利,必须设法抑制自噪声以估计目标方位。文中采用自适应噪声抵消器对平台自噪声进行抑制,介绍了两种噪声抵消的方法,然后进行方位估计。同时对这两种方法作了计算机仿真。结果表明,两种方法都能有效地抑制平台本地噪声。     

单矢量水听器方位频率联合估计新方法

旋转不变子空间法和多重信号分类法需假设背景噪声为独立的高斯白噪声或自相关矩阵已知,当条件不满足时算法的性能急剧下降。针对这一问题,根据矢量水听器多通道输出的特点,提出了一种基于平行因子模型的单矢量水听器方位频率联合估计方法。首先利用矢量水听器各个通道t时刻和t+1时刻的输出数据,计算声压和各振速不同组合时的四阶累积量,并构建三阶平行因子模型;然后分析了 PARAFAC 模型低秩分解的唯一性条件并利用三线性交替最小二乘算法得到了单矢量水听器阵列流形和相位延迟估计,进而得到目标的方位和频率估计。与旋转不变子空间法和多重信号分类法相比,该方法不需要子空间估计和谱峰搜索,在高斯噪声和拉普拉斯噪声背景下对多目标的分辨能力好于ESPRIT算法。仿真和实测数据的分析结果证明了算法的有效性。     

改进的最小均方误差语音增强算法的研究

针对传统最小均方误差谱幅度估计（MMSE—STSA．minimum mean-square error-short time spectral amplitude）语音增强算法无法有效的跟踪非平稳噪声变化的问题,对一种改进的MMSE-STSA语音增强算法进行了研究和仿真。该算法对背景噪声的估计利用加权噪声估计方法：采用一个非线性函数根据带噪语音信噪比（SNR．signal—to-noise ratio）的变化计算得到相应的加权因子并作用于带噪语音信号,对加权的带噪语音求平均得到估计的背景噪声。算法中的谱增益修正,还可以抑制低信噪比时的残留噪声以及避免对带噪语音的过抵消。实验结果表明,该方法能很好的跟踪非平稳噪声的变化,不仅在增强性能上有很好的效果,同时降低了语音的失真。     

利用二阶循环平稳性的信道自适应盲辨识

提出了利用二阶循环平稳性信道盲辨识的自适应求解方法。通过递推更新循环相关函数,从而完成了循环相关矩阵的自适应估计。利用共轭梯度搜索的方法实现信道的自适应盲辨识,并讨论了梯度算法中步长选取的问题,提出了可变步长选取的方法。由于采用二阶循环统计量,因此算法既简单又可以很好的抑制平稳加性有色噪声。计算机仿真表明,在低信噪比情况下,自适应算法能很好的工作。     

基于M序列调相信号的矢量水听器方位估计

利用矢量水听器接收确知脉冲信号来确定声源方位时,脉冲信号形式可以考虑采用M序列调相信号,具有良好的抗多途效应,本文提出了各向同性噪声场下利用矢量水听器接收确知脉冲信号进行方位估计的最大似然方位估计.矢量水听器的水池试验结果证明在多途效应比较严重的情况下,采用M序列调相信号利用以上方法进行方位估计仍可取得较高了测向的精度.     

利用时空平均法改善DOA估计性能

在信号子空间类DOA估计算法中 ,自相关矩阵估计的优劣决定了DOA估计的性能 ,但是常规自相关矩阵估计方法是对阵列输出信号做时间平均。然而对于均匀线列阵这一特殊阵型 ,其阵列输出自相关矩阵的具有To eplitz结构。根据均匀线列阵信号的时空平稳特性 ,本文提出一种时空平均的方法来改善自相关矩阵的估计质量 ,即对阵列的自相关矩阵做时间和空间两次平均 ,从而提高了DOA估计的性参 ;仿真结果表明 :在低信噪比或低快拍数的条件下能够明显提高DOA估计性能     

基于过程神经网络算法的航天器非平稳随机振动时频分析

采用时变参数模型对航天器某时段非平稳随机振动信号(NSRVS)进行建模分析,利用过程神经元网络(PNN)求解模型的时变参数并以此确定信号的时变自功率谱密度.计算结果表明:由PNN估计的NSRVS时变参数与自相关Levinson法估计的该参数基本一致,但前者建模物理意义明确,和传统的方法相比避免了计算信号的自相关矩阵,减少了存储空间,提高了频率分辨率和计算速度.     

An efficient simulation algorithm for non-Gaussian nonstationary processes

A novel algorithm is proposed for simulating univariate non-Gaussian nonstationary processes (NNP) with the specified evolutionary power spectral density (EPSD)/nonstationary auto-correlation function (NACF) and first four-order time-varying marginal moments (TVMMs). The sample realizations of the target NNP are generated as the outputs from a specific time-varying auto-regressive (TVAR) model via filtering the non-Gaussian and nonstationary white noise inputs. These white noise inputs are also non-Gaussian and nonstationary, and their first four-order TVMMs are predetermined using an approach developed herein according to the specified EPSD/NACF and first four-order TVMMs of the outputs. The conventional Johnson transformation is updated to accommodate the nonstationary cases for producing desired white noise inputs. This algorithm is developed from the linear filtering method (LFM), and inherits the simplicity and high efficiency from LFM. It fills the gaps in LFM-based algorithms for simulating NNP. Two numerical examples, i.e., a ground motion acceleration and a downburst velocity, are presented to fully demonstrate the capabilities of the proposed algorithm by comparing the simulation statistics with the targets.     

稳健协方差矩阵重构波束形成算法

针对信号导向向量失配以及接收数据协方差矩阵存在误差会导致传统的自适应波束形成器产生能损失的问题,提出了一种基于干扰加噪声协方差矩阵重构的稳健波束形成算法.该算法通过对信源来波角度范围进行Capon谱估计得出重构信源协方差矩阵,并通过特征分解以及子空间性质得出信源的导向向量,然后利用重构所得信源导向向量计算出信源功率以及...     

解析声能流Capon空间谱估计

文章中把解析函数理论用于声矢量信号分析，提出了一个新概念“解析声能流”，给出了单矢量传感器输出的解析声能流表达式。并将这个新概念用于描述声矢量阵输出，得到了一种新的矢量阵输出模型“伪解析声能流”。利用该模型，推导了声矢量阵的Capon空间谱估计的表达式。理论分析和计算机仿真表明：1、解析声能流的实部和虚部具有正交的偶极子指向性，并且可以用移相的方法来旋转其主极大方向；2、在平面波和二维质点振速条件下，解析声能流将矢量叠加统一描述在它的相位中，从而可以综合描述声场的相位干涉和矢量结构；3、基于“伪解析声能流”模型，传统的Capon空间谱估计算法无需改动就可以直接用于声矢量信号处理，与传统的Capon空间谱估计算法相比，具有全空间无模糊定向能力、更低的旁瓣和对短时信号有更强的处理能力。     

A successive parameter estimation algorithm for chirplet signal decomposition

In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, and object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation.     

Damping Identification of Bridges Under Nonstationary Ambient Vibration

This research focuses on identifying the damping ratio of bridges using nonstationary ambient vibration data. The damping ratios of bridges in service have generally been identified using operational modal analysis (OMA) based on a stationary white noise assumption for input signals. However, most bridges are generally subjected to nonstationary excitations while in service, and this violation of the basic assumption can lead to uncertainties in damping identification. To deal with nonstationarity, an amplitude-modulating function was calculated from measured responses to eliminate global trends caused by nonstationary input. A natural excitation technique (NExT)-eigensystem realization algorithm (ERA) was applied to estimate the damping ratio for a stationarized process. To improve the accuracy of OMA-based damping estimates, a comparative analysis was performed between an extracted stationary process and nonstationary data to assess the effect of eliminating nonstationarity. The mean value and standard deviation of the damping ratio for the first vertical mode decreased after signal stationarization.     

Estimations of frequency and its drift rate

This paper presents an analysis of frequency and its drift rate estimation by the difference method, the least-squares method, and the Kalman filter. Error formulas are derived for all five noise processes: white phase, flicker phase, white frequency, flicker frequency, and random walk frequency. The error formulas show the relationship between the estimate error and the noise spectral density coefficients, the same interval τ, and the data number N. Because of the existence of some nonstationary noise processes, a large data number may not yield a good estimation. One should choose an appropriate sample interval and data number so as to control the estimate error. An optimal solution based on the Kalman filter is presented     

Linear shift algorithm for detecting nonstationary signals on the back-ground of stationary noise

A nonparametric algorithm for detecting useful nonstationary signals on the background of stationary noise is described. The algorithm properties were investigated with the aid of test signals describing monopole and dipole sources. Translated from Izmeritel'naya Tekhnika, No. 3, pp. 45–47, March, 1997.     

一种快速自适应噪声谱估计方法

提出了一种快速自适应的噪声谱估计方法。该方法在Doblinger和Cohen的噪声谱估计方法基础上,提出了改进算法,修正了带噪语音子带中语音存在的判断门限和噪声谱更新公式,估计的噪声谱既能快速适应背景噪声变化,又能保证准确性。实验结果表明,结合文中噪声谱估计方法构成的语音增强系统能有效抑制带噪语音中平稳和非平稳噪声,对于噪声突变的情况也适用。     

Data-Aided Timing Recovery for Recording Channels With Data-Dependent Noise

In high-density data storage systems, noise becomes highly correlated and data dependent as a result of media noise, channel nonlinearities, and front-end filters. In such environments, conventional timing recovery schemes will exhibit large residual timing jitter and, especially, data-dependent timing jitter. This paper presents a new data-aided timing recovery algorithm for data storage systems with data-dependent noise. We derive a maximum-likelihood timing recovery scheme based on a data-dependent Gauss-Markov model of the noise. The timing recovery algorithm incorporates data-dependent noise prediction parameters in the form of linear prediction filters and prediction error variances. Moreover, because noise can be nonstationary in practice, we propose an adaptive algorithm to estimate and track the noise prediction parameters. Simulation results, for an idealized optical storage channel incorporating a simple model of media noise, illustrate the merits of our algorithm